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Cross-Modal Multivariate Pattern Analysis
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A multi-modal approach for identifying schizophrenia using cross-modal attention.

Gowtham Premananth, Yashish M Siriwarden, Philip Resnik

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 5, 2025
    PubMed
    Summary

    This study introduces a multi-modal system combining audio, video, and text to classify schizophrenia. The novel approach significantly improves accuracy in distinguishing patients with positive symptoms from healthy individuals.

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    Area of Science:

    • Neuroscience
    • Computer Science
    • Psychiatry

    Background:

    • Schizophrenia classification often relies on limited data modalities.
    • Distinguishing schizophrenia, particularly positive symptoms, requires nuanced analysis of communication patterns.

    Purpose of the Study:

    • To develop and evaluate a multi-modal system for classifying schizophrenia using audio, video, and text data.
    • To enhance the accuracy of schizophrenia detection by integrating diverse communication features.

    Main Methods:

    • Extracted low-level facial action units (video) and vocal tract variables (audio).
    • Computed high-level coordination features from audio-video data.
    • Utilized context-independent text embeddings from speech transcriptions.
    • Developed a fusion model combining segment-to-session classifiers (audio/video) with a Hierarchical Attention Network (HAN) for text, incorporating cross-modal attention.

    Main Results:

    • The proposed multi-modal system demonstrated superior performance compared to prior state-of-the-art methods.
    • Achieved an 8.53% improvement in weighted average F1 score.

    Conclusions:

    • Multi-modal analysis of human communication offers a powerful approach for schizophrenia classification.
    • The developed system shows significant potential for improving diagnostic accuracy in schizophrenia research.